Integrated Orientation Texture Feature and Its Application in Multi-spectral Image Fusion

Zhang Yi-fan; He Ming-yi

doi:10.3724/SP.J.1146.2005.00515

Volume 29 Issue 1

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(1): 81-86

Zhang Yi-fan, He Ming-yi. Integrated Orientation Texture Feature and Its Application in Multi-spectral Image Fusion[J]. Journal of Electronics & Information Technology, 2007, 29(1): 81-86. doi: 10.3724/SP.J.1146.2005.00515

Citation:

Zhang Yi-fan, He Ming-yi. Integrated Orientation Texture Feature and Its Application in Multi-spectral Image Fusion[J]. Journal of Electronics & Information Technology, 2007, 29(1): 81-86. doi: 10.3724/SP.J.1146.2005.00515

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Integrated Orientation Texture Feature and Its Application in Multi-spectral Image Fusion

doi: 10.3724/SP.J.1146.2005.00515

Received Date: 2005-05-08
Rev Recd Date: 2005-09-27
Publish Date: 2007-01-19

Abstract

Abstract

Texture feature is one kind of important image features in multi-spectral images except with spectral characteristics. In this paper, the characteristics of orientation texture features are analyzed. And then the concept and method of integrated orientation texture feature are proposed and developed. On the basis of this, Significant Central Coefficient (SCC) image fusion algorithm based on integrated orientation texture feature in redundant wavelet field is proposed. The algorithm can combine spectral and morphological information in multi-spectral images successfully. It can pertain the spectral characteristics of source images and also take effect on fusion result of texture features into account. The new multi-spectral image fusion algorithm is carried out with emphases on the novelty of the fusion algorithm and the demonstration by using both simulated and real multi-spectral images. The subjective qualitative evaluation and objective quantitative analysis of the experimental results are made, appearing that the new algorithm can fuse the information and retain the texture features of source images more effectively compared with several existing fusion algorithms for multi-spectral images.

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References(1)

References

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